RiverSoftAVG Genetic Algorithms & Programming Component Library
The Genetic Algorithms & Programming Component Library (GACL) is a powerful genetic algorithms and genetic programming solution for Delphi and Appmethod Win32, Win64, OSX, iOS and Android! Designed for Delphi 2010-Berlin (Win32/Win64/OSX/iOS/Android) and Appmethod (Object Pascal), the GACL provides simple yet powerful components for designing, evolving, and using genetic algorithms and genetic programs. The Genetic Algorithms & Programming Component Library is now in version 5.x. Version 5.x is a free upgrade for users who bought the GACL after Dec 1st, 2014. Users who bought the GACL before December 1st, 2014 need to purchase an upgrade from the orders page.
Genetic Algorithms and Genetic Programming help you automatically solve a wide range of problems, from optimization and search problems using genetic algorithms to data fitting, prediction and modelling, or decision strategy and game control using genetic programming.
See the GACL Version History page for full details on what has changed.
For Delphi 2010-Berlin (Win32/Win64/OSX/iOSX/Android) and Appmethod (Object Pascal) (Earlier versions are available for Delphi 2009 and earlier)
Genetic algorithms (GA) are computer science techniques that seek to solve optimization or search problems. They are inspired by evolutionary biology and approach the search problem as a task of evolving a group or population of candidate individuals through successive generations, selecting fitter (or better) child individuals for each generation, until a solution is found. It uses evolutionary biology techniques such as inheritance, mutation, selection, and crossover (also called recombination).
Genetic algorithms have been used in bioinformatics, phylogenetics, computational science, engineering, economics, chemistry, manufacturing, mathematics, physics, pharmacometrics and other fields.
Genetic programming (GP) is a computer science method, inspired by evolutionary biology, for automatically solving problems, without having to know or define the form or structure of optimum problem structure beforehand. You define the basic building blocks (functions, constants, and variables) of the problem and then the component does the rest. Genetic programming solves problems by evolving a group or population of candidate individuals through successive generations, selecting fitter (or better) child individuals for each generation, until a solution is found. It uses evolutionary biology techniques such as inheritance, mutation, selection, and crossover (also called recombination).
Genetic Programming is a specialization of genetic algorithms where each individual is a computer program. It has found success as a automatic programming tool, a machine learning tool or an automatic problem-solving engine. Genetic programming can be used for Curve Fitting, Data Modelling and Symbolic Regression; Decision Strategy, Game Control, and Industrial Process Control; Image and Signal Processing; and Financial Trading, Time Series Prediction and Economic Modelling.
Check out the evaluation demo on the Downloads page.
The demo has no limitations except:
*** The DEMO version is for EVALUATION PURPOSES ONLY ***
Demo applications using the GACL are also available on the Downloads page.
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